Ballast fouling is detrimental to railroad track functions. Ground-penetrating radar (GPR), a nondestructive testing tool, has been used to assess ballast-fouling conditions. However, processing the extensive amount of data to quantify ballast conditions is challenging. Although several approaches have been developed, each of them has certain disadvantages, such as being unable to detect fouling without a clear interface between clean and fouled ballast and being user dependent. To overcome these disadvantages, a new approach based on wavelet transform was investigated. Laboratory tests were conducted to collect GPR data on ballast with controlled fouling levels. The data were processed by choosing the proper mother wavelet, selecting appropriate wavelet decomposition coefficients, and de-noising the signal. Standard deviation (SD) values of the processed wavelet detail coefficients were calculated. On the basis of the scattering theory, the resulting SD value, which represents the scattering intensity of the signal, is an indication of the fouling level. The laboratory results clearly show that the SD value decreases as the fouling level increases. The effectiveness of the proposed approach was then validated by field data from the Orin subdivision in Wyoming. By a comparison of in situ ground truth data and GPR measurements, the wavelet transform was proved to be an effective approach to quantifying ballast-fouling conditions. This approach allows for fouling assessment without the presence of a clear interface between clean and fouled ballast and reduces user dependency. In addition, the new approach is capable of processing GPR data automatically and continuously and provides the entire fouling profile along the tracks.


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    Title :

    Ground-Penetrating Radar Data to Develop Wavelet Technique for Quantifying Railroad Ballast–Fouling Conditions


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2012-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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